For an Autonomous Vehicle (AV) to make decisions and drive independently on urban streets, the problem at hand can be broken down into many phases, two of which are perception and prediction. Perception refers to the process of extracting valuable information from the environment using data collected by sensors such as LIDAR and camera. This includes detection of cars, ped estrians, lanes among many objects. Prediction refers to the process of tracking all the known objects and predicting the possible future actions so as to enable the autonomous vehicle to make informed decisions.
The current project intends to create a software platform solution that solves the communication between organizations in the Music Industry. Overall, the platform uses third party systems to achieve the requirements of the problem statement. Moreover, the platform is built over abstraction levels to integrate different components. The research of the integration of these parties including blockchain technologies will solve the data transmission and accountability responsible for the payment distribution of value in the specific use case of the Partner in the Music Industry.
The current land registry system in Ontario lacks efficiency and transparency; and is susceptible to information quality problems due to lack of a uniform and integrated system to record and share real-time data about land property transactions across stakeholder organizations. To overcome such issues, many countries are turning to blockchain technology to enable land registration transactions.
yodelME is a Kelowna, British Columbia based firm that develops network solutions. They are particularly interested in providing communication capabilities over ad hoc networks, i.e., networks with node mobility and topology changing capabilities, deployed in regions with limited pre-existing communications infrastructure. In recent years, they have primarily concentrated on providing enhanced communication capabilities for emergency services personnel in remote areas, such as that required during natural disasters, i.e., floods and forest fires.
Personalized health is increasingly gaining public attention in the media as the future of healthcare. Personalized health is the idea that medical treatment will be tailored to the individual based on their predicted response or risks of disease. Omics analysis, defined as the universal detection of different classes of biological molecules, has the potential to direct personalized health delivery and enhance lifestyle changes, such as changes in diet and exercise habits, that may prevent disease development.
The objective of the project is to investigate how machine learning techniques can be used to detect anomalies in volumes of transactions. This requires the student to conduct a literature review about the topic as well as experimenting with a subset of selected machine learning techniques. The results from the research could help the partner organization in improving in place mechanisms used to detect anomalies in volume of transactions.
Ultra-reliable and low latency communication is increasingly an important aspect of future wireless communications. Specifically, in the context of mission critical communications for large-scale networks of sensors and actuators in automated and/or remote-control applications, low-latency wireless communication with high level of determinism is a vital element. The key performance indicators for such use case are in sharp contrast to the current broadband communications, since latency and reliability are paramount but lower data rates can be tolerated.
Tracking the component configuration and modifications to aircraft within the commercial airline business presents a challenge for manufacturers such as Bombardier with currently available methods. This research problem is significant to the aerospace industry to construct efficient maintenance schedules for different aircraft and to properly evaluate system reliability for safety purposes. The objective of this research is to determine a new methodology for tracking and determining probable component configuration for commercial aircraft.
Machine Learning has just started to be applied to the Legal Domain. ROSS Intelligence makes it possible for legal professionals to work faster and more effectively. Advanced Recommender Systems have not been previously applied in the Legal Domain. Yet state of the art models such as ones using Deep Collaborative Filtering have proven to be very effective on very sparse datasets. This project will evaluate the effectiveness of state-of-the-art Recommender System techniques applied to the legal domain in the hopes of improving the precision and recall of the current Q&A system.
The proposed research aims to find a solution to the connectivity of the massive number of devices that are essential for the monitoring and regulation of power generation and demands in power grids. Power grids present specific challenges that the intern will take into consideration and seek to propose solutions to the connectivity and security of these devices. Hence, the proposed research will contribute to the efficient implementation and optimization of two-way communications capabilities in power grids.